Top 5 open source video generation models

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# Lights, camera…

With launch I see AND Soravideo generation has reached a modern level. Creators are experimenting demanding and teams are integrating these tools into their marketing processes. However, there is a drawback: most closed systems collect data and apply apparent or hidden watermarks that mark the results as AI-generated. If you value privacy, control, and on-device workflow, open source models are your best bet, and several of them can now rival Veo’s results.

In this article, we will review five of the best video generation models, providing technical knowledge and a demo video to facilitate you evaluate their video generation capabilities. Each model is available on Face Hugging and can run locally for Comfortable or your preferred desktop AI applications.

# 1. Wan 2.2 A14B

Van 2.2 modernizes its diffusion framework with a Mixture-of-Experts (MoE) architecture that divides denoising in individual time steps among specialized experts, increasing effective capacity without computational cost. The team also developed aesthetic labels (e.g. lighting, composition, contrast, color tone) to provide greater control over the “cinematic” look. Compared to Wan 2.1, training has been significantly scaled (+65.6% images, +83.2% videos), improving movement, semantics and aesthetics.

Wan 2.2 has the highest performance among both open and closed systems. You can browse the A14B text-to-video and image-to-video repositories at Hugging Face: Wan-AI/Wan2.2-T2V-A14B AND Wan-AI/Wan2.2-I2V-A14B

# 2. Hunyuan movie

Hunyuan video is an open 13B video model trained in a spatio-temporal latent space using a 3D causal variational autoencoder (VAE). Its transformer uses a “dual stream to single stream” design: text and video tokens are first processed independently with full attention and then combined, while a decoder-only multimodal LLM serves as a text encoder to improve instruction execution and capture details.

The open source ecosystem includes code, weights, single and multi-GPU inference (xDiT), FP8 weights, Diffusers and integration with ComfyUI, a Built Penguin Video demo and benchmark.

# 3. Mochi 1

Mochi 1 is a 10B asymmetric diffusion transformer (AsymDiT) trained from scratch, released under Apache 2.0. It connects to an asymmetric VAE module that compresses 8×8 video spatially and 6x temporally into a 12-channel latent signal, prioritizing visual capacity over text using a single T5-XXL encoder.

In initial evaluations, the Genmo team positions Mochi 1 as a state-of-the-art open model with high motion fidelity and high traction, aiming to fill the gap in closed systems.

# 4. LTX Video

LTX Video is an image-to-video generator based on DiT (Diffusion Transformer), built for speed: it creates videos at 30 frames per second at 1216 x 704 resolution faster than real time, trained on a huge, diverse dataset to balance motion and image quality.

The offering includes multiple variants: 13B dev, 13B distilled, 2B distilled and FP8 quantized versions, as well as spatial and temporal scaling modules and ready-to-use ComfyUI workflows. If you are optimizing for speedy iterations and clear motion from a single image or tiny conditioning sequence, LTX will be an attractive choice.

# 5. CogVideoX-5B

CogVideoX-5B is the higher fidelity brother of the 2B baseline, trained on bfloat16 and recommended for running on bfloat16. It generates 6-second clips at 8 fps with a fixed resolution of 720×480 and supports English prompts up to 226 tokens.

Models documentation shows the expected video random access memory (VRAM) for single and multi-GPU inference, typical run times (e.g. around 90 seconds for 50 steps on a single H100), and how diffuser optimizations such as CPU offloading and VAE tiling/slicing impact memory and speed.

# Selecting a video generation model

Here are some general tips to facilitate you choose the right video generation model for your needs.

• If you want a cinematic look and 720p/24 on one 4090: Wan 2.2 (A14B for basic tasks; hybrid TI2V 5B for capable 720p/24)
• If you need a huge, universal T2V/I2V base with robust traffic and a full set of open source software (OSS) tools: HunyuanVideo (13B, xDiT parallelism, FP8 weights, diffusers/ComfyUI)
• If you need a liberal, hackable, state-of-the-art preview (SOTA) with state-of-the-art traffic and a clear research plan: Mochi 1 (10B AsymmDiT + AsymmVAE, Apache 2.0)
• If you care about real-time I2V and editing capabilities with ComfyUI upscalers and workflows: LTX-Video (30fps at 1216×704, multiple 13B/2B and FP8 variants)
• If you need capable 6s 720×480 T2V, solid diffuser support and quantization down to compact VRAM: CogVideoX-5B

Abid Ali Awan (@1abidaliawan) is a certified data science professional who loves building machine learning models. Currently, he focuses on creating content and writing technical blogs about machine learning and data science technologies. Abid holds a Master’s degree in Technology Management and a Bachelor’s degree in Telecommunications Engineering. His vision is to build an AI product using a graph neural network for students struggling with mental illness.